Java Code Examples for weka.core.Instance#deleteAttributeAt()

public Instance filterInstance(Instance ins){
	int nosDeleted=0;
	int nosKept=0;
	int dataPos=0;
	Instance newIns=new DenseInstance(ins);
		//Advance to the next to keep
	while(dataPos<newIns.numAttributes()-1 && nosKept<attsToKeep.length){
		while(dataPos!=attsToKeep[nosKept]-nosDeleted && dataPos<newIns.numAttributes()-1){
			newIns.deleteAttributeAt(dataPos);
			nosDeleted++;
		}
		nosKept++;
		dataPos++;
	}
	while(dataPos<newIns.numAttributes()-1)
		newIns.deleteAttributeAt(dataPos);
	return newIns;
	
}
 

/**
 * ConvertInstance - Convert an Instance to multi-class format by deleting all but one of the label attributes.
 * @param	x	incoming Instance
 * @return	the converted Instance
 */
public Instance convertInstance(Instance x) {

	int L = x.classIndex();

	//Copy the original instance
	Instance x_ = (Instance) x.copy(); 
	x_.setDataset(null);

	//Delete all class attributes
	for (int i = 0; i < L; i++)
		x_.deleteAttributeAt(0);

	//Add one of those class attributes at the begginning
	x_.insertAttributeAt(0);

	//Hopefully setting the dataset will configure that attribute properly
	x_.setDataset(m_InstancesTemplate);

	return x_;
}
 

protected void update(Instance x) throws Exception {

			Instance x_ = (Instance)x.copy();
			x_.setDataset(null);

			// delete all except one (leaving a binary problem)
			// delete all the attributes (and track where our index ends up)
			int c_index = this.value;
			for(int i = excld.length-1; i >= 0; i--) {
				x_.deleteAttributeAt(excld[i]);
				if (excld[i] < this.index)
					c_index--; 
			}
			x_.setDataset(this._template);

			((UpdateableClassifier)this.classifier).updateClassifier(x_);

			if (next != null)
				next.update(x);
		}
 

protected void classify(Instance test) throws Exception {
	// copy
	Instance copy = (Instance)test.copy();
	copy.setDataset(null);

	// delete attributes we don't need
	for(int i = excld.length-1; i >= 0; i--) {
		copy.deleteAttributeAt(this.excld[i]);
	}

	//set template
	copy.setDataset(this._template);

	//set class
	test.setValue(this.index,(int)(this.classifier.classifyInstance(copy))); 

	//carry on
	if (next!=null) next.classify(test);
}
 

/**
 * LinkTransform - prepare 'x' for testing at a node 'j' of the chain, by excluding 'exl'.
 * @param	x		instance
 * @param	excl	indices of labels which are NOT parents of j
 * @param	_D		the dataset template to use
 * @return	the transformed instance
 */
public static Instance linkTransformation(Instance x, int excl[], Instances _D) {
	// copy
	Instance copy = (Instance)x.copy();
	copy.setDataset(null);

	// delete attributes we don't need
	for(int i = excl.length-1; i >= 0; i--) {
		copy.deleteAttributeAt(excl[i]);
	}

	//set template
	copy.setDataset(_D);

	return copy;
}
 

@Override
public Instance transform(Instance inst) {
    Instance newInst= null;
    try {
        newInst = pca.convertInstance(inst);
        while(newInst.numAttributes()-1>numAttributesToKeep)
            newInst.deleteAttributeAt(newInst.numAttributes()-2);

    } catch (Exception e) {
        e.printStackTrace();
    }

    return newInst;
}
 

protected void classify(Instance test) throws Exception {
	// copy
	Instance copy = (Instance)test.copy();
	copy.setDataset(null);

	// delete attributes we don't need
	for(int i = excld.length-1; i >= 0; i--) {
		copy.deleteAttributeAt(this.excld[i]);
	}

	//set template
	copy.setDataset(this._template);

	// round
	for(int k = 0; k < this.j; k++) {
		copy.setValue(j,Math.round(copy.value(k)));
	}

	//set class
	double dist[] = this.classifier.distributionForInstance(copy);
	int max_index = Utils.maxIndex(dist);
	confidences[this.index] = dist[max_index];
	test.setValue(this.index,max_index);

	//carry on
	if (next!=null) next.classify(test);
}
 

/**
 * Convert a multi-label instance into a multi-class instance, according to a template.
 */
public static Instance convertInstance(Instance x, int L, Instances template) {
	Instance x_ = (Instance) x.copy(); 
	x_.setDataset(null);
	for (int i = 0; i < L; i++)
		x_.deleteAttributeAt(0);
	x_.insertAttributeAt(0);
	x_.setDataset(template);
	return x_;
}
 

/**
 * meka2mulan - Move L label attributes from the beginning to end of attribute space of an Instance. 
 * Necessary because MULAN assumes label attributes are at the end, not the beginning.
 * (the extra time for this process is not counted in the running-time analysis of published work).
 */
public static final Instance meka2mulan(Instance x, int L) {
	x.setDataset(null);
	for(int j = 0; j < L; j++) {
		x.insertAttributeAt(x.numAttributes());
		x.deleteAttributeAt(0);
	}
	return x;
}
 

/**
 * Delete attributes from an instance 'x' indexed by 'indicesToRemove[]'.
 * @param	x					instance
 * @param	indicesToRemove		array of attribute indices
 * @return	the modified dataset
 */
public static final Instance deleteAttributesAt(Instance x, int indicesToRemove[]) {//, boolean keep) {
	Arrays.sort(indicesToRemove);
	for(int j = indicesToRemove.length-1; j >= 0; j--) {
		x.deleteAttributeAt(indicesToRemove[j]);
	}
	return x;
}
 

@Override
public void buildClassifier(Instances D) throws Exception {
  	testCapabilities(D);
  	
	int L = D.classIndex();

	//Create header
	Instances D_ = new Instances(D,0,0);

	//Delete the old class attributes
	for (int j = 0; j < L; j++)
		D_.deleteAttributeAt(0); 

	//Make the new class attribute
	FastVector classes = new FastVector(L);
	for (int j = 0; j < L; j++)
		classes.addElement("C"+j);

	//Add the new class attribute
	D_.insertAttributeAt(new Attribute("ClassY",classes),0);
	D_.setClassIndex(0);

	//Loop through D again
	for (int i = 0; i < D.numInstances(); i++) {
		for (int j = 0; j < L; j++) {
			if((int)D.instance(i).value(j) > 0) {
				// make a copy here ...
				Instance x_ = (Instance)D.instance(i).copy();
				x_.setDataset(null);
				// make it multi-class, and set the appropriate class value ...
				for (int k = 1; k < L; k++)
					x_.deleteAttributeAt(1); 
				x_.setDataset(D_);
				x_.setClassValue(j); // (*) this just ponts to the right index
				D_.add(x_);
			}
		}
	}

	//Save the template
	m_InstancesTemplate = new Instances(D_,0);

	//Build
	if(getDebug())  System.out.println("Building classifier "+m_Classifier.getClass().getName()+" on "+D_.numInstances()+" instances (originally "+D.numInstances()+")");
	m_Classifier.buildClassifier(D_);

}